Why data science often fails - The dangers Mum and Dad never warned you aboutYou might have seen presentations and advice on success factors for data science. But we think that many are not specific enough to really help you in building successful data science teams or executing successful data science programs.
Marcel and Carsten collected the pitfalls and success factors they think really matter – beyond the usual high level (project) management advice.

Making Scout24 ready for AI (EN)Artificial Intelligence will change the world in the next three years. To keep track with this exponentially growing technology it is important to make everyone in your company ready for AI. Since mid 2018 Scout24 is on a journey to make every Scoutie ready for AI and to prepare the complete business for AI. I will describe our journey, learnings of the last months and present exciting AI projects which help Scout24 to make the difference.

The Scout24 Data Platform: a technical deep dive (EN)The Scout24 Data Platform powers all reporting, ad hoc analytics and machine learning products at AutoScout24 and ImmobilienScout24. In this talk, I will take a technical deep dive into our modern, cloud-based big data platform. I will discuss our evolution of approaches to ingestion, ETL, access control, reporting and machine learning with a focus on in-the-trenches learnings gained from our many failures and successes as we migrated from a traditional Oracle Data Warehouse to an AWS-based data lake.

Building and Deploying Your Image Classification Model in One Hour (EN)During this workshop, you will learn how you can use Dataiku DSS to build a Deep Learning model for Image classification without a single line of code or deep learning expertise. Step by step, you’ll explore different ways to leverage existing state-of-the art deep learning models in your own projects. Throughout the workshop, you will build your own image classifier using a pre-trained model and deploy it to a production environment to perform real-time predictions on images.

Using Deep Learning to rank and tag millions of hotel images (EN)At idealo.de (a leading price comparison website in Europe), we have a dedicated service to provide hotel price comparisons (hotel.idealo.de). For each hotel, we receive dozens of images and face the challenge of composing image galleries that are attractive and at the same time help our users to make informed decisions. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need both an attractiveness assessment and a tag (e.g. a “bathroom” or “bedroom” tag)...

Quantum Machine Learning puts AI on Steroids (EN)Currently, we are at the beginning of a new era of computing, which will have a massive impact on society as a whole - it is called Quantum Computing.
This technology will greatly influence the development and boost the performance of AI. Furthermore, it will allow for the first time to solve very complex problems e.g. in the fields of material and medical research or cryptography...

Using ML to fashion the business of online retailing (EN)We will discuss how Zalando and other online retail businesses are leveraging Machine Learning to create the best experience for customers, while using technology to drive Data Governance and Privacy. There will be an overview of our ML use cases and a deep dive into how we use Cloud based tools to create the framework for a Machine Learning journey.

Productionizing Machine Learning Models - Lessons Learned in the Hadoop Ecosystem and the Way Ahead (EN)The deployment of machine learning models can be challenging. Especially in the context of distributed systems: Python being the dominant language among data scientists creates frictions when integrating with JVM-based tools such as Spark or managing application dependencies on clusters of heterogenous machines. Many data scientists developing on such systems struggle with the subtleties of these challenges. This presentation will share lessons learned working on large-scale Hadoop clusters and examine the most promising approaches to alleviate common issues.

HPCC Systems - A Hidden Champion in Big Data Processing (EN)HPCC Systems is an open-source big data system. It is developed by LexisNexis® Risk Solutions and used for credit ratings, fraud detection, anti money laundering, and many more enterprise applications. The system is based on the query language ECL that gets compiled down to C++ and machine code to be automatically deployed and executed on a compute cluster. In this workshop, we\'ll walk through the architecture of HPCC Systems, its capabilities and popular extensions (ML, visualization, connectors to other systems), and learn basics of its main query language ECL. Users of other big data systems such as Spark or Flink will feel familiar with the dataflow-oriented query language, the data types, and the available operators. However, the workshop is open to everyone interested in the topic without prior knowledge.

How to build Containerized Architecture for Deep Learning (EN)When it comes to AI data scientists/engineers tend to focus on tools. Though the data platform that enables these tools is equally important, it’s often overlooked. In fact, 90% of the effort required for success in ML is not the algorithm - it\'s the data logistics. In this workshop we will talk about common architecture blueprints to integrate AI in your data centers and how the right data platform choice can make all the difference in launching your AI use case into production!

A Practical Guide to AI, Machine Learning, and Data Science (EN)Eager to adopt AI in your enterprise? Get an inside look at Oracle\'s platform AI solutions including new approaches using machine learning and data science. There is a seismic shift among Enterprises to harness more and more transformational technologies to improve their customer experiences, drive greater revenues, and lower operational costs. In this talk, we will cover Oracle’s strategy and solutions for AI across all of Oracle cloud platform including the pervasive mix of AI, Machine Learning across all of our services portfolio. See new advancements in data science and learn ways to develop and build new AI-based applications. We\'ll hear some of our customer success stories to date, and provide a personal pathway for enterprises to adopt these technologies based on where they are in their transformational journey.

Smart Training of ML Models in Audio- and Video Mining (EN)The need for training data for machine learning algorithms is still incredibly high and is constantly growing with the growing use of artificial intelligence. Many companies are therefore faced with the challenge of reluctant to use methods such as deep learning because training the models would be too complex and time-consuming. At the same time, however, models available on the market are usually not directly applicable and do not fit optimally to one\'s own needs...

Deep dive into Machine Learning with the Oracle Platform (incl. live demo) (EN)In this workshop we will do a in-depth walkthrough on how you can use machine learning by a single click as part of your visualizations and analysis, how you can build Machine Learning models using Python/R code with notebooks against Big Data and Relational Data and even how you can do machine learning using good old SQL. Everything using Oracle’s state of the art Cloud Infrastructure.

Multi-Cloud Architecture for Bigdata and analytics (EN)The recognition of data as enterprise asset, digitization initiatives and emergence of the new market of data products, demand robust analytical application industrialization with no single point of dependence. Using Multi-Cloud Architecture for Bigdata and analytics we try to give a solution to this upcoming challenge.

Two years of Data Science in a Fujitsu factory (EN)When talking about data science projects we often focus on algorithms and results. But when you start a data science project from scratch in a factory there are a lot of different problems that need to be tackled: where to install a sensor in order to get data, how to involve external parties as the maker of the machines, defining what is acceptable data quality.... In this talk I want to discuss the challenges we faced and what we learned when we used data science in the Fujitsu factory in Augsburg in order to improve run time and quality.

Predictive Road Condition (EN)The driving situations, the dynamic condition of the vehicle and the road condition are of high relevance in order to return the vehicle from critical situations, in particular with adverse weather-related road conditions, into a safe condition. The subject of this presentation is to determine road conditions and to derive a prediction of these with black data.

Anomaly detection in customs area (EN)The search for anomalous transactions was performed manually on historical data. A check for previously unknown anomalies and attribute constellations is hardly possible with manually defined rules. The aim of the project is to automatically detect anomalies and unwanted deviations using intelligent and learning procedures. The project therefore focused on advanced technologies (e.g. neural networks).

Why data science often fails - The dangers Mum and Dad never warned you aboutYou might have seen presentations and advice on success factors for data science. But we think that many are not specific enough to really help you in building successful data science teams or executing successful data science programs.
Marcel and Carsten collected the pitfalls and success factors they think really matter – beyond the usual high level (project) management advice.

Making Scout24 ready for AI (EN)Artificial Intelligence will change the world in the next three years. To keep track with this exponentially growing technology it is important to make everyone in your company ready for AI. Since mid 2018 Scout24 is on a journey to make every Scoutie ready for AI and to prepare the complete business for AI. I will describe our journey, learnings of the last months and present exciting AI projects which help Scout24 to make the difference.

The Scout24 Data Platform: a technical deep dive (EN)The Scout24 Data Platform powers all reporting, ad hoc analytics and machine learning products at AutoScout24 and ImmobilienScout24. In this talk, I will take a technical deep dive into our modern, cloud-based big data platform. I will discuss our evolution of approaches to ingestion, ETL, access control, reporting and machine learning with a focus on in-the-trenches learnings gained from our many failures and successes as we migrated from a traditional Oracle Data Warehouse to an AWS-based data lake.

Building and Deploying Your Image Classification Model in One Hour (EN)During this workshop, you will learn how you can use Dataiku DSS to build a Deep Learning model for Image classification without a single line of code or deep learning expertise. Step by step, you’ll explore different ways to leverage existing state-of-the art deep learning models in your own projects. Throughout the workshop, you will build your own image classifier using a pre-trained model and deploy it to a production environment to perform real-time predictions on images.

Using Deep Learning to rank and tag millions of hotel images (EN)At idealo.de (a leading price comparison website in Europe), we have a dedicated service to provide hotel price comparisons (hotel.idealo.de). For each hotel, we receive dozens of images and face the challenge of composing image galleries that are attractive and at the same time help our users to make informed decisions. Given that we have millions of hotel offers, we end up with more than 100 million images for which we need both an attractiveness assessment and a tag (e.g. a “bathroom” or “bedroom” tag)...

Quantum Machine Learning puts AI on Steroids (EN)Currently, we are at the beginning of a new era of computing, which will have a massive impact on society as a whole - it is called Quantum Computing.
This technology will greatly influence the development and boost the performance of AI. Furthermore, it will allow for the first time to solve very complex problems e.g. in the fields of material and medical research or cryptography...

Using ML to fashion the business of online retailing (EN)We will discuss how Zalando and other online retail businesses are leveraging Machine Learning to create the best experience for customers, while using technology to drive Data Governance and Privacy. There will be an overview of our ML use cases and a deep dive into how we use Cloud based tools to create the framework for a Machine Learning journey.

Productionizing Machine Learning Models - Lessons Learned in the Hadoop Ecosystem and the Way Ahead (EN)The deployment of machine learning models can be challenging. Especially in the context of distributed systems: Python being the dominant language among data scientists creates frictions when integrating with JVM-based tools such as Spark or managing application dependencies on clusters of heterogenous machines. Many data scientists developing on such systems struggle with the subtleties of these challenges. This presentation will share lessons learned working on large-scale Hadoop clusters and examine the most promising approaches to alleviate common issues.

HPCC Systems - A Hidden Champion in Big Data Processing (EN)HPCC Systems is an open-source big data system. It is developed by LexisNexis® Risk Solutions and used for credit ratings, fraud detection, anti money laundering, and many more enterprise applications. The system is based on the query language ECL that gets compiled down to C++ and machine code to be automatically deployed and executed on a compute cluster. In this workshop, we\'ll walk through the architecture of HPCC Systems, its capabilities and popular extensions (ML, visualization, connectors to other systems), and learn basics of its main query language ECL. Users of other big data systems such as Spark or Flink will feel familiar with the dataflow-oriented query language, the data types, and the available operators. However, the workshop is open to everyone interested in the topic without prior knowledge.

How to build Containerized Architecture for Deep Learning (EN)When it comes to AI data scientists/engineers tend to focus on tools. Though the data platform that enables these tools is equally important, it’s often overlooked. In fact, 90% of the effort required for success in ML is not the algorithm - it\'s the data logistics. In this workshop we will talk about common architecture blueprints to integrate AI in your data centers and how the right data platform choice can make all the difference in launching your AI use case into production!

A Practical Guide to AI, Machine Learning, and Data Science (EN)Eager to adopt AI in your enterprise? Get an inside look at Oracle\'s platform AI solutions including new approaches using machine learning and data science. There is a seismic shift among Enterprises to harness more and more transformational technologies to improve their customer experiences, drive greater revenues, and lower operational costs. In this talk, we will cover Oracle’s strategy and solutions for AI across all of Oracle cloud platform including the pervasive mix of AI, Machine Learning across all of our services portfolio. See new advancements in data science and learn ways to develop and build new AI-based applications. We\'ll hear some of our customer success stories to date, and provide a personal pathway for enterprises to adopt these technologies based on where they are in their transformational journey.

Smart Training of ML Models in Audio- and Video Mining (EN)The need for training data for machine learning algorithms is still incredibly high and is constantly growing with the growing use of artificial intelligence. Many companies are therefore faced with the challenge of reluctant to use methods such as deep learning because training the models would be too complex and time-consuming. At the same time, however, models available on the market are usually not directly applicable and do not fit optimally to one\'s own needs...

Deep dive into Machine Learning with the Oracle Platform (incl. live demo) (EN)In this workshop we will do a in-depth walkthrough on how you can use machine learning by a single click as part of your visualizations and analysis, how you can build Machine Learning models using Python/R code with notebooks against Big Data and Relational Data and even how you can do machine learning using good old SQL. Everything using Oracle’s state of the art Cloud Infrastructure.

Multi-Cloud Architecture for Bigdata and analytics (EN)The recognition of data as enterprise asset, digitization initiatives and emergence of the new market of data products, demand robust analytical application industrialization with no single point of dependence. Using Multi-Cloud Architecture for Bigdata and analytics we try to give a solution to this upcoming challenge.

Two years of Data Science in a Fujitsu factory (EN)When talking about data science projects we often focus on algorithms and results. But when you start a data science project from scratch in a factory there are a lot of different problems that need to be tackled: where to install a sensor in order to get data, how to involve external parties as the maker of the machines, defining what is acceptable data quality.... In this talk I want to discuss the challenges we faced and what we learned when we used data science in the Fujitsu factory in Augsburg in order to improve run time and quality.

Predictive Road Condition (EN)The driving situations, the dynamic condition of the vehicle and the road condition are of high relevance in order to return the vehicle from critical situations, in particular with adverse weather-related road conditions, into a safe condition. The subject of this presentation is to determine road conditions and to derive a prediction of these with black data.

Anomaly detection in customs area (EN)The search for anomalous transactions was performed manually on historical data. A check for previously unknown anomalies and attribute constellations is hardly possible with manually defined rules. The aim of the project is to automatically detect anomalies and unwanted deviations using intelligent and learning procedures. The project therefore focused on advanced technologies (e.g. neural networks).